This method is an alias of pp_expect.brmsfit
with additional arguments for obtaining summaries of the computed samples.
# S3 method for brmsfit
fitted(
object,
newdata = NULL,
re_formula = NULL,
scale = c("response", "linear"),
resp = NULL,
dpar = NULL,
nlpar = NULL,
nsamples = NULL,
subset = NULL,
sort = FALSE,
summary = TRUE,
robust = FALSE,
probs = c(0.025, 0.975),
...
)An object of class brmsfit.
An optional data.frame for which to evaluate predictions. If
NULL (default), the original data of the model is used.
NA values within factors are interpreted as if all dummy
variables of this factor are zero. This allows, for instance, to make
predictions of the grand mean when using sum coding.
formula containing group-level effects to be considered in
the prediction. If NULL (default), include all group-level effects;
if NA, include no group-level effects.
Either "response" or "linear".
If "response", results are returned on the scale
of the response variable. If "linear",
results are returned on the scale of the linear predictor term,
that is without applying the inverse link function or
other transformations.
Optional names of response variables. If specified, predictions are performed only for the specified response variables.
Optional name of a predicted distributional parameter. If specified, fitted values of this parameters are returned.
Optional name of a predicted non-linear parameter. If specified, fitted values of this parameters are returned.
Positive integer indicating how many posterior samples should
be used. If NULL (the default) all samples are used. Ignored if
subset is not NULL.
A numeric vector specifying the posterior samples to be used.
If NULL (the default), all samples are used.
Logical. Only relevant for time series models.
Indicating whether to return predicted values in the original
order (FALSE; default) or in the order of the
time series (TRUE).
Should summary statistics be returned
instead of the raw values? Default is TRUE..
If FALSE (the default) the mean is used as
the measure of central tendency and the standard deviation as
the measure of variability. If TRUE, the median and the
median absolute deviation (MAD) are applied instead.
Only used if summary is TRUE.
The percentiles to be computed by the quantile
function. Only used if summary is TRUE.
Further arguments passed to extract_draws
that control several aspects of data validation and prediction.
An array of predicted mean response values.
If summary = FALSE the output resembles those of
pp_expect.brmsfit.
If summary = TRUE the output depends on the family: For categorical
and ordinal families, the output is an N x E x C array, where N is the
number of observations, E is the number of summary statistics, and C is the
number of categories. For all other families, the output is an N x E
matrix. The number of summary statistics E is equal to 2 +
length(probs): The Estimate column contains point estimates (either
mean or median depending on argument robust), while the
Est.Error column contains uncertainty estimates (either standard
deviation or median absolute deviation depending on argument
robust). The remaining columns starting with Q contain
quantile estimates as specifed via argument probs.
In multivariate models, an additional dimension is added to the output which indexes along the different response variables.
# NOT RUN {
## fit a model
fit <- brm(rating ~ treat + period + carry + (1|subject),
data = inhaler)
## extract fitted values
fitted_values <- fitted(fit)
head(fitted_values)
## plot fitted means against actual response
dat <- as.data.frame(cbind(Y = standata(fit)$Y, fitted_values))
ggplot(dat) + geom_point(aes(x = Estimate, y = Y))
# }
# NOT RUN {
# }
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